According to the literature [1]}, [2]}, [3]}, in the test process, for each user, we randomly select 50 items that the user has not interacted with, and randomly select 1 item that has interacted with the user in test set. The recommended list is composed of these 51 items, and the 51 items are sorted to reduce test's complexity. In model training, we use AUC [4]} as an evaluation indicator to determine the best parameters. When evaluating model performance, we use Precision@K [4]}, Recall@K [4]}and MRR@K [4]} as evaluation indicators. We calculate these indicators for each user, and calculate the average value when K = {1, 2, 4, 5, 6, 8, 10}. Since situation does not recommend too many items in actual, we choose these values for K. Generally, higher values of these indicators indicate better performance. AUC characterizes the overall performance of recommendation algorithm and covers the performance of all different recommendation list lengths [4]}. Therefore, we use AUC as an evaluation indicator during training.
